Extract and Merge Batches/Image patches (tf/torch), fast and self-contained digital image processing and deep learning model training.
- Extract patches
- Merge the extracted patches to obtain the original image back.
Update 0.2.2 (New Functionalities)
- Handling 1D spectral and 3D volumetric data structures, thanks to antonyvam.
- Batch processing support for 1D, 2D, 3D (image/pixel + voxel/volumetric) data added.
- Bug fixes for multi-dimensional image patch merging for
C > 3.
Update 0.2.0 (New Functionalities)
-
Handling of
tensorflow/pytorchBatched images of shapeBxCxHxW->pytorchorBxHxWxC->tf. C can be any number not limited to just RGB channels. -
Modes added for mergeing patches.
overwrite: next patch will overwrite the overlapping area of the previous patch.max: maximum value of overlapping area at each pixel will be written.min: minimum value of overlapping area at each pixel will be written.avg: mean/average value of overlapping area at each pixel will be written.
-
Patching via providing Indices.
-
Strided patching thanks to Andreasgejlm
python >= 3.6
numpy
math
- Extracting Patches
- Merging Patches
- Voxel/Volumetric Data patching
- 1D spectral Data patching
- Strided Patching
- Batched Patching
- Patching via Providing Indices
from empatches import EMPatches
import imgviz # just for plotting
# get image either RGB or Grayscale
img = cv2.imread('../digits.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# load module
emp = EMPatches()
img_patches, indices = emp.extract_patches(img, patchsize=512, overlap=0.2)
# displaying 1st 10 image patches
tiled= imgviz.tile(list(map(np.uint8, img_patches)),border=(255,0,0))
plt.figure()
plt.imshow(tiled)
Now we can perform our operation on each patch independently and after we are done we can merge them back together.
'''
pseudo code
'''
# do some processing, just store the patches in the list in same order
img_patches_processed = some_processing_func(img_patches)
# or run your deep learning model on patches independently and then merge the predictions
img_patches_processed = model.predict(img_patches)
'''For now lets just flip channels'''
img_patches[1] = cv2.cvtColor(img_patches[1], cv2.COLOR_BGR2RGB)
After processing the patches if you can merge all of them back in original form as follows,
merged_img = emp.merge_patches(img_patches, indices, mode='max') # or
merged_img = emp.merge_patches(img_patches, indices, mode='min') # or
merged_img = emp.merge_patches(img_patches, indices, mode='overwrite') # or
merged_img = emp.merge_patches(img_patches, indices, mode='avg') # or
# display
plt.figure()
plt.imshow(merged_img.astype(np.uint8))
plt.title(Your mode)
img_patches, indices = emp.extract_patches(img, patchsize=512, overlap=0.2, stride=128)
tiled= imgviz.tile(list(map(np.uint8, img_patches)),border=(255,0,0))
plt.figure()
plt.imshow(tiled.astype(np.uint8))
plt.title('Strided patching')
# first generate a sample data
def midpoints(x):
sl = ()
for i in range(x.ndim):
x = (x[sl + np.index_exp[:-1]] + x[sl + np.index_exp[1:]]) / 2.0
sl += np.index_exp[:]
return x
r, g, b = np.indices((17, 17, 17)) / 16.0
rc = midpoints(r)
gc = midpoints(g)
bc = midpoints(b)
# define a sphere about [0.5, 0.5, 0.5]
sphere = ((rc - 0.5)**2 + (gc - 0.5)**2 + (bc - 0.5)**2 < 0.5**2).astype(int)
ax = plt.figure().add_subplot(projection='3d')
ax.voxels(sphere)
plt.title(f'Voxel 3D data: {sphere.shape} shape')Extract patches from voxel 3D data.
emp = EMPatches()
patches, indices = emp.extract_patches(sphere, patchsize=8, overlap=0.0, stride=None, vox=True)
ax = plt.figure().add_subplot(projection='3d')
ax.voxels(patches[1])
plt.title(f'Patched Voxel 3D data: {patches[0].shape} shape')
for i in range(len(patches)):
print(patches[i].shape)
mp = emp.merge_patches(patches, indices)###############___VOXEL DATA___ setting vox to True ########################
## shape indices in xyz dimension
>> (8, 8, 8) (0, 8, 0, 8, 0, 8)
>> (8, 8, 8) (0, 8, 0, 8, 8, 16)
>> (8, 8, 8) (8, 16, 0, 8, 0, 8)
>> (8, 8, 8) (8, 16, 0, 8, 8, 16)
>> (8, 8, 8) (0, 8, 8, 16, 0, 8)
>> (8, 8, 8) (0, 8, 8, 16, 8, 16)
>> (8, 8, 8) (8, 16, 8, 16, 0, 8)
>> (8, 8, 8) (8, 16, 8, 16, 8, 16)
Here the output shape is 8x8x8 i.e. the croping is also done in D/C dimension unlike when we are doing image croping/patching in that case the output would have shape 8x8x3 (RGB) or 8x8 (grayscale), and incides would be like.
###############___PIXEL DATA___ -> setting vox to False ########################
## shape indices in xy dimension
>> (8, 8, 16) (0, 8, 0, 8)
>> (8, 8, 16) (8, 16, 0, 8)
>> (8, 8, 16) (0, 8, 8, 16)
>> (8, 8, 16) (8, 16, 8, 16)
x1 = np.linspace(0.0, 5.0)
y1 = np.cos(5 * np.pi * x1) * np.exp(-x1)
plt.plot(y1)
plt.title('1D spectra')
emp = EMPatches()
patches, indices = emp.extract_patches(y1, patchsize=8, overlap=0.0, stride=None)
ax1 = plt.subplot(1)
plt.plot(patches[0]) # 0th patch
ax2 = plt.subplot(2, sharex=ax1, sharey=ax1)
plt.plot(patches[2]) # 2nd pathc
plt.suptitle('patched 1D spectra')
# merge again
mp = emp.merge_patches(patches, indices)
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batch : Batch of images of shape either BxCxHxW -> pytorch or BxHxWxC -> tf to extract patches from in list(list1, list2, ...), where, list1->([H W C], [H W C], ...) and so on.
-
patchsize : size of patch to extract from image only square patches can be extracted for now.
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overlap (Optional): overlap between patched in percentage a float between [0, 1].
-
stride (Optional): Step size between patches
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type (Optional): Type of batched images tf or torch type
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batch_patches : a list containing lists of extracted patches of images.
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batch_indices : a list containing lists of indices of patches in order, whihc can be used at later stage for 'merging_patches'.
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merged_batch : a np array of shape BxCxHxW -> pytorch or BxHxWxC -> tf.
from empatches import BatchPatching
bp = BatchPatching(patchsize=512, overlap=0.2, stride=None, typ='torch')
# extracging
batch_patches, batch_indices = bp.patch_batch(batch) # batch of shape BxCxHxW, C can be any number 3 or greater
plt.imshow(batch_patches[1][2])
plt.title('3rd patch of 2nd image in batch')
# merging
# output will be of shpae depending on typ variable
# BxCxHxW -> torch or BxHxWxC -> tf
merged_batch = bp.merge_batch(batch_patches, batch_indices, mode='avg')
# accessing the merged images
plt.imshow(merged_batch[1,...].astype(np.uint8))
plt.title('2nd merged image in batch')
NOTE in this case merging is not supported.
from empatches import patch_via_indices
img = cv2.imread('./digit.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (1024, 512))
i = [(0, 512, 0, 256), # 1st patch dims/indices
(0, 256, 310, 922),# 2nd patch dims/indices
(0, 512, 512, 768)]# 3rd patch dims/indices
img_patches = patch_via_indices(img, indices)
# plotting
tiled= imgviz.tile(list(map(np.uint8, img_patches)),border=(255,0,0))
plt.figure()
plt.imshow(tiled.astype(np.uint8))
plt.title('patching via providing indices')
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